Infrared Satellite Measurements Research Articles

Pyrogenic HONO seen from space: insights from global IASI observations

Abstract. Nitrous acid (HONO) is a key atmospheric component, acting as a major source of the hydroxyl radical (OH), the primary oxidant in the Earth's atmosphere. However, understanding its spatial and temporal variability remains a significant challenge. Recent TROPOspheric Monitoring Instrument (TROPOMI)/Sentinel-5 Precursor (S5P) ultraviolet–visible (UV–Vis) measurements of fresh fire plumes shed light on the impact of global pyrogenic HONO emissions. Here, we leverage Infrared Atmospheric Sounding Interferometer (IASI)/MetOp's global infrared satellite measurements, complementing midday TROPOMI observations with morning and evening overpasses, to detect and retrieve pyrogenic HONO in 2007–2023. Employing a sensitive detection method, we identify HONO enhancements within concentrated fire plumes worldwide. Most detections are in the Northern Hemisphere (NH) mid- and high latitudes, where intense wildfires and high injection heights favour HONO detection. IASI's nighttime measurements yield 10-fold more HONO detections than daytime measurements, emphasizing HONO's extended lifetime in the absence of photolysis during the night. The annual detection count increases by at least 3–4 times throughout the IASI time series, mirroring the recent surge in intense wildfires at these latitudes. Additionally, we employ a neural-network-based algorithm for retrieving pyrogenic HONO total columns from IASI and compare them with TROPOMI in the same fire plumes. The results demonstrate TROPOMI's efficacy in capturing HONO enhancements in smaller fire plumes and in proximity to fire sources, while IASI's morning and evening overpasses enable HONO measurements further downwind, highlighting the survival of HONO or its secondary formation along long-range transport in smoke plumes.

Greenhouse Gas Forcing and Climate Feedback Signatures Identified in Hyperspectral Infrared Satellite Observations

AbstractGlobal greenhouse gas forcing and feedbacks are the primary causes of climate change but have limited direct observations. Here we show that continuous, stable, global, hyperspectral infrared satellite measurements (2003–2021) display decreases in outgoing longwave radiation (OLR) in the CO2, CH4, and N2O absorption bands and increases in OLR in the window band and H2O absorption bands. By conducting global line‐by‐line radiative transfer simulations with 2003–2021 meteorological conditions, we show that increases in CO2, CH4, and N2O concentrations caused an instantaneous radiative forcing and stratospheric cooling adjustment that decreased OLR. The climate response, comprising surface and atmospheric feedbacks to radiative forcings and unforced variability, increased OLR. The spectral trends predicted by our climate change experiments using our general circulation model identify three bedrock principles of the physics of climate change in the satellite record: an increasing greenhouse effect, stratospheric cooling, and surface‐tropospheric warming.

Constraining industrial ammonia emissions using hyperspectral infrared imaging

Atmospheric emissions of reactive nitrogen in the form of nitrogen dioxide (NO2) and ammonia (NH3) worsen air quality and upon deposition, dramatically affect the environment. Recent infrared satellite measurements have revealed that NH3 emitted by industries are an important and underestimated emission source. Yet, to assess these emissions, current satellite sounders are severely limited by their spatial resolution. In this paper, we analyse measurement data recorded in a series of imaging surveys that were conducted over industries in the Greater Berlin area (Germany). On board the aircraft were the Telops Hyper-Cam LW, targeting NH3 measurements in the longwave infrared at a resolution of 4 m and the SWING+ spectrometer targeting NO2 measurements in the UV–Vis at a resolution of 180 m.Two flights were carried out over German’s largest production facility of synthetic NH3, urea and other fertilizers. In both cases, a large NH3 plume was observed originating from the factory. Using a Gaussian plume model to take into account plume rise and dispersion, coupled with well-established radiative transfer and inverse methods, we retrieve vertical column densities. From these, we calculate NH3 emission fluxes using the integrated mass enhancement and cross-sectional flux methods, yielding consistent emissions of the order of 2200 t yr−1 for both flights, assuming constant fluxes across the year. These estimates are about five times larger than those reported in the European Pollutant Release and Transfer Register (E-PRTR) for this plant. In the second campaign, a co-emitted NO2 plume was measured, likely related to the production of nitric acid at the plant.A third flight was carried out over an area comprising the cities of Staßfurt and Bernburg. Several small NH3 plumes were seen, one over a production facility of mineral wool insulation, one over a sugar factory and two over the soda ash plants in Staßfurt and Bernburg. A fifth and much larger plume was seen to originate from the sedimentation basins associated with the soda ash plant in Staßfurt, indicating rapid volatilization of ammonium rich effluents. We use the different measurement campaigns to simulate measurements of Nitrosat, a potential future satellite sounder dedicated to the sounding of reactive nitrogen at a resolution of 500 m. We demonstrate that such measurements would allow accurately constraining emissions in a single overpass, overcoming a number of important drawbacks of current satellite sounders.

Cloudy-sky land surface temperature from VIIRS and MODIS satellite data using a surface energy balance-based method

Land surface temperature (LST) has been effectively retrieved from thermal infrared (TIR) satellite measurements under clear-sky conditions. However, TIR satellite data are often severely contaminated by clouds, which cause spatiotemporal discontinuities and low retrieval accuracy in the LST products. Several solutions have been proposed to fill the “gaps”; however, a majority of these possess constraints. For example, fusion methods with microwave data suffer from coarse spatial resolution and diverse land cover types while spatial-temporal interpolation methods neglect cloudy cooling effects. We developed a novel method to estimate cloudy-sky LST from polar-orbiting satellite data based on the surface energy balance (SEB) principle. First, the hypothetical clear-sky LST of missing or likely cloud-contaminated pixels was reconstructed by assimilating high-quality satellite retrievals into a time-evolving model built from reanalysis data using a Kalman filter data assimilation algorithm. Second, clear-sky LST was hypothetically corrected by accounting for cloud cooling based on SEB theory. The proposed method was applied to Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, and further validated using ground measurements of fourteen sites from SURFRAD, BSRN, and AmeriFlux in 2013. VIIRS LST recovered from cloud gaps exhibited a root mean square error (RMSE) of 3.54 K, a bias of −0.36 K, R2 of 0.94, and sample size (N) of 2411, comparable to the accuracy of clear-sky LST products and cloudy-sky LST estimation from MODIS (RMSE of 3.69 K, bias of −0.45 K, R2 of 0.93, and N of 2398). Thus, the proposed method performs well across different sensors, seasons, and land cover types. The abnormal retrieval values caused by cloud contamination were also corrected in the proposed method. The overall accuracy was better than the downscaled cloudy-sky LST retrieved from passive microwave (PMW) observations and former SEB-based cloudy-sky LST estimation methods. Validation using time-series measurements showed that the all-sky LST time series, including both clear- and cloudy-sky retrievals, can capture realistic variability without sudden abruptions or discontinuities. RMSE values for the all-sky LST varied from 2.54 to 4.15 K at the fourteen sites. Spatially continuous LST maps over the Contiguous United States were compared with corresponding maps from PMW data in the winter and summer of 2018, exhibiting similar spatial patterns but with additional spatial details. Moreover, sensitivity analysis suggested that the reconstruction of clear-sky LST dominantly impacts the accuracy of cloudy-sky LST estimation. The proposed method can be potentially implemented in similar satellite sensors for global real-time production.

A Novel Approach to Estimating Time-Averaged Volcanic SO2 Fluxes from Infrared Satellite Measurements

Long-term continuous time series of SO2 emissions are considered critical elements of both volcano monitoring and basic research into processes within magmatic systems. One highly successful framework for computing these fluxes involves reconstructing a representative time-averaged SO2 plume from which to estimate the SO2 source flux. Previous methods within this framework have used ancillary wind datasets from reanalysis or numerical weather prediction (NWP) to construct the mean plume and then again as a constrained parameter in the fitting. Additionally, traditional SO2 datasets from ultraviolet (UV) sensors lack altitude information, which must be assumed, to correctly calibrate the SO2 data and to capture the appropriate NWP wind level which can be a significant source of error. We have made novel modifications to this framework which do not rely on prior knowledge of the winds and therefore do not inherit errors associated with NWP winds. To perform the plume rotation, we modify a rudimentary computer vision algorithm designed for object detection in medical imaging to detect plume-like objects in gridded SO2 data. We then fit a solution to the general time-averaged dispersion of SO2 from a point source. We demonstrate these techniques using SO2 data generated by a newly developed probabilistic layer height and column loading algorithm designed for the Cross-track Infrared Sounder (CrIS), a hyperspectral infrared sensor aboard the Joint Polar Satellite System’s Suomi-NPP and NOAA-20 satellites. This SO2 data source is best suited to flux estimates at high-latitude volcanoes and at low-latitude, but high-altitude volcanoes. Of particular importance, IR SO2 data can fill an important data gap in the UV-based record: estimating SO2 emissions from high-latitude volcanoes through the polar winters when there is insufficient solar backscatter for UV sensors to be used.

Tropospheric ozone retrieval from thermal infrared nadir satellite measurements: Towards more adaptability of the constraint using a self-adapting regularization

We developed a Self-Adapting Constraint Retrieval Scheme (SACRS) to retrieve ozone profiles from nadir infrared satellite measurements. In this algorithm, the constraint is variable in altitude and adapted automatically for each individual measurement. The algorithm is tested on synthetic observations representing the future IASI-NG satellite observations and considering either ozonesonde measurements or chemistry-transport model ozone simulations to represent the true ozone (pseudo-reality). The ozone retrievals are evaluated mainly for the troposphere with a specific focus on the lower troposphere between the surface and 6 km. Compared to a previous algorithm based on a fixed constraint retrieval scheme (FCRS), the biases, correlation and error estimates are improved with the SACRS. The bias is reduced by 40% and the correlation coefficient increases from 0.72 to 0.80. The SACRS algorithm also leads to an enhanced sensitivity in the lower troposphere with degrees of freedom for signal up to 0.83, increased by 11% compared to the FCRS. The SACRS performs especially well where current algorithms usually fail, namely for polar and tropical air masses. The bias is reduced from 8.6% to 0.5% in the troposphere (surface-9 km) when considering polar cases and from 24.4% to 10.1% in the upper troposphere - lower troposphere column (12–18 km) in the tropics.

Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption

Abstract. Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and the ash plume is usually vertically located in a narrow band, directly assimilating the 2-D ash mass loadings in a three-dimensional (3-D) volcanic ash model (with an integral observational operator) can usually introduce large artificial/spurious vertical correlations.In this study, we look at an approach to avoid the artificial vertical correlations by not involving the integral operator. By integrating available data of ash mass loadings and cloud top heights, as well as data-based assumptions on thickness, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The 3-D SOO makes the analysis step of assimilation comparable in the 3-D model space.Ensemble-based DA is used to assimilate the extracted measurements of ash concentrations. The results show that satellite DA with SOO can improve the estimate of volcanic ash state and the forecast. Comparison with both satellite-retrieved data and aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts for the distal part of the Eyjafjallajökull volcano is about 6 h.

Global Daily High-Resolution Satellite-Based Foundation Sea Surface Temperature Dataset: Development and Validation against Two Definitions of Foundation SST

This paper describes a global, daily sea surface temperature (SST) analysis based on satellite microwave and infrared measurements. The SST analysis includes a diurnal correction method to estimate foundation SST (SST free from diurnal variability) using satellite sea surface wind and solar radiation data, frequency splitting to reproduce intra-seasonal variability and a quality control procedure repeated twice to avoid operation errors. An optimal interpolation method designed for foundation SST is applied to blend the microwave and infrared satellite measurements. Although in situ SST measurements are not used for bias correction adjustments in the analysis, the output product, with a spatial grid size of 0.1°, has an accuracy of 0.48 ∘ C and 0.46 ∘ C compared to the in situ foundation SST measurements derived by drifting buoys and Argo floats, respectively. The same quality against the two types of in situ foundation SST (drifters and Argo) suggests that the two definitions of foundation SST proposed by past studies can provide same-quality information about the sea surface state underlying the diurnal thermocline.

Improved Quality of MODIS Sea Surface Temperature Retrieval and Data Coverage Using Physical Deterministic Methods

Sea surface temperature (SST) retrievals from satellite imager measurements are often performed using only two or three channels, and employ a regression methodology. As there are 16 thermal infrared (IR) channels available for MODIS, we demonstrate a new SST retrieval methodology using more channels and a physically deterministic method, the modified total least squares (MTLS), to improve the quality of SST. Since cloud detection is always a part of any parameter estimation from IR satellite measurements, we hereby extend our recently-published novel cloud detection technique, which is based on both functional spectral differences and radiative transfer modeling for GOES-13. We demonstrate that the cloud detection coefficients derived for GOES-13 are working well for MODIS, while further improvements are made possible by the extra channels replacing some of the previous tests. The results are compared with available operational MODIS SST through the Group for High Resolution SST website–the data themselves are originally processed by the NASA Goddard Ocean Biology Processing Group. It is observed the data coverage can be more than doubled compared to the currently-available operational product, and at the same time the quality can be improved significantly. Two other SST retrieval methods, offline-calculated coefficients using the same form of the operational regression equation, and radiative transfer based optimal estimation, are included for comparison purposes.

Characterising volcanic cycles at Soufriere Hills Volcano, Montserrat: Time series analysis of multi-parameter satellite data

The identification of cyclic volcanic activity can elucidate underlying eruption dynamics and aid volcanic hazard mitigation. Whilst satellite datasets are often analysed individually, here we exploit the multi-platform NASA A-Train satellite constellation to cross-correlate cyclical signals identified using complementary measurement techniques at Soufriere Hills Volcano (SHV), Montserrat. In this paper we present a Multi-taper (MTM) Fast Fourier Transform (FFT) analysis of coincident SO2 and thermal infrared (TIR) satellite measurements at SHV facilitating the identification of cyclical volcanic behaviour. These measurements were collected by the Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) (respectively) in the A-Train. We identify a correlating cycle in both the OMI and MODIS data (54–58days), with this multi-week feature attributable to episodes of dome growth. The ~50day cycles were also identified in ground-based SO2 data at SHV, confirming the validity of our analysis and further corroborating the presence of this cycle at the volcano. In addition a 12day cycle was identified in the OMI data, previously attributed to variable lava effusion rates on shorter timescales. OMI data also display a one week (7–8days) cycle attributable to cyclical variations in viewing angle resulting from the orbital characteristics of the Aura satellite. Longer period cycles possibly relating to magma intrusion were identified in the OMI record (102-, 121-, and 159days); in addition to a 238-day cycle identified in the MODIS data corresponding to periodic destabilisation of the lava dome. Through the analysis of reconstructions generated from cycles identified in the OMI and MODIS data, periods of unrest were identified, including the major dome collapse of 20th May 2006 and significant explosive event of 3rd January 2009. Our analysis confirms the potential for identification of cyclical volcanic activity through combined analysis of satellite data, which would be of particular value at poorly monitored volcanic systems.

Detection of the Grímsvötn 2011 volcanic eruption plumes using infrared satellite measurements

AbstractInfrared satellite measurements were used to track the volcanic plume from Grímsvötn, Iceland in 2011. Following the explosive eruption, the plume was sheared by the atmosphere into two distinct volcanic clouds, one was ash‐rich and the other was mainly composed of sulphur dioxide (SO2) gas. Satellite retrievals of the volcanic ash were found to be consistent with an andesitic/basaltic composition, providing a framework for composition evaluation immediately following an eruption. SO2 was detectable for several weeks following the eruption highlighting the importance of monitoring volcanic clouds for a prolonged period to manage the associated risk of air traffic.

Volcanic ash infrared signature: porous non-spherical ash particle shapes compared to homogeneous spherical ash particles

Abstract. The reverse absorption technique is often used to detect volcanic ash clouds from thermal infrared satellite measurements. From these measurements effective particle radius and mass loading may be estimated using radiative transfer modelling. The radiative transfer modelling usually assumes that the ash particles are spherical. We calculated thermal infrared optical properties of highly irregular and porous ash particles and compared these with mass- and volume-equivalent spherical models. Furthermore, brightness temperatures pertinent to satellite observing geometry were calculated for the different ash particle shapes. Non-spherical shapes and volume-equivalent spheres were found to produce a detectable ash signal for larger particle sizes than mass-equivalent spheres. The assumption of mass-equivalent spheres for ash mass loading estimates was found to underestimate mass loading compared to morphologically complex inhomogeneous ash particles. The underestimate increases with the mass loading. For an ash cloud recorded during the Eyjafjallajökull 2010 eruption, the mass-equivalent spheres underestimate the total mass of the ash cloud by approximately 30% compared to the morphologically complex inhomogeneous particles.

Diagnosis of high‐resolution upper ocean dynamics from noisy sea surface temperatures

The noise present in infrared satellite measurements of sea surface temperature (SST) hampers the use of surface quasi‐geostrophic (SQG) equations to diagnose ocean dynamics at high resolutions. Here we propose a methodology to reduce the contribution of noise when diagnosing surface vorticity, divergence, and vertical velocity from SST able to retain the dynamics at scales of a few kilometers. It is based on the use of denoising techniques with curvelets as basis functions and the application of a selective low‐pass filters to improve the reconstruction of surface upwelling/downwelling patterns. First, it is tested using direct numerical simulations of SQG turbulence and then applied to diagnose low‐frequency vertical velocity patterns from real MODIS (Moderate Resolution Imaging Spectroradiometer) images. The methodology here presented, which is not tied to the validity of SQG equations nor to the use of SST, is quite general and can be applied to a wide range of measurements and dynamical frameworks.

An assessment of the diurnal variation of upper tropospheric humidity in reanalysis data sets

AbstractDiurnal variations of upper tropospheric humidity (UTH) in five different reanalysis data sets are compared over convective land and ocean regions and evaluated using multiple satellite observations as a reference. All reanalysis data sets reproduce the day‐night contrast of upper tropospheric humidity and the land‐ocean contrast in the diurnal amplitude. The infrared satellite measurements indicate a slightly later diurnal minimum over land relative to most reanalyses and the microwave satellite measurements, suggesting that cloud masking of the infrared radiances may introduce a small (∼3 h) bias in the phase. One reanalysis exhibits a substantially different diurnal cycle over land which is inconsistent with both infrared and microwave satellite measurements and other reanalysis products. This product also exhibits a different covariance between vertical velocity, cloud water, and humidity than other reanalyses, suggesting that the phase bias is related to deficiencies in the parameterization of moist convective processes.

Intercomparison of polar ozone profiles by IASI/MetOp sounder with 2010 Concordiasi ozonesonde observations

Abstract. Validation of ozone profiles measured from a nadir looking satellite instrument over Antarctica is a challenging task due to differences in their vertical sensitivity with ozonesonde measurements. In this paper, ozone observations provided by the Infrared Atmospheric Sounding Interferometer (IASI) instrument onboard the polar-orbiting satellite MetOp are compared with ozone profiles collected between August and October 2010 at McMurdo Station, Antarctica, during the Concordiasi measurement campaign. The main objective of the campaign was the satellite data validation. With this aim 20 zero-pressure sounding balloons carrying ozonesondes were launched during this period when the MetOp satellite was passing above McMurdo. This makes the dataset relevant for comparison, especially because the balloons covered the entire altitude range of IASI profiles. The validation methodology and the collocation criteria vary according to the availability of global positioning system auxiliary data with each electro-chemical cell ozonesonde observation. The relative mean difference is shown to depend on the vertical range investigated. The analysis shows a good agreement in the troposphere (below 10 km) and middle stratosphere (25–40 km), where the differences are lower than 10%. However a significant positive bias of about 10–26% is estimated in the lower stratosphere at 10–25 km, depending on altitude. The positive bias in the 10–25 km range is consistent with previously reported studies comparing in situ data with thermal infrared satellite measurements. This study allows for a better characterization of IASI-retrieved ozone over the polar region during ozone depletion/recovery processes.

Automated retrievals of volcanic ash and dust cloud properties from upwelling infrared measurements

AbstractA fully automated, globally applicable algorithm to retrieve ash and dust cloud properties from infrared satellite measurements is presented. The algorithm, which will serve as the official operational algorithm of the next generation Geostationary Operational Environmental Satellite (GOES‐R), utilizes an optimal estimation framework that allows uncertainties in the measurements and forward model to be taken into account and uncertainty estimates for each of the retrieved parameters to be determined. The retrieval approach is globally applicable because background atmospheric water vapor, surface temperature, and surface emissivity are explicitly accounted for on a pixel‐by‐pixel basis. The retrieval is demonstrated using the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on‐board the Second Generation Meteosat. Ash clouds from the 2010 eruption of Eyjafjallajökull in Iceland and the 2010 eruption of Soufriere Hills in the eastern Caribbean and a Saharan dust cloud were analyzed, and the accuracy of the retrieval was evaluated using spaceborne lidar measurements. The validation analysis shows that the retrieved ash/dust cloud height, cloud emissivity, and effective particle radius generally agrees well with lidar measurements, especially when volcanic ash clouds are assumed to be composed of andesite and dust clouds composed of kaolinite.

A New Technique Using Infrared Satellite Measurements to Improve the Accuracy of the CALIPSO Cloud-Aerosol Discrimination Method

In this paper, we develop a new technique called the brightness temperature difference cloud and aerosol discrimination algorithm (BTD CAD) that uses thermal infrared satellite measurements to improve the accuracy of the cloud-aerosol lidar and infrared pathfinder satellite observations (CALIPSO) CAD algorithm. It has been shown that the CALIPSO CAD algorithm can misclassify dense dust as cloud because the CALIPSO two-wavelength backscatter lidar operates at 532 and 1064 nm where very similar scattering properties are known to exist between dense dust and cloud. Therefore, we use the 11 and 12 μm thermal infrared channels from both the moderate resolution imaging spectroradiometer (MODIS) and the spinning enhanced visible and infrared imager (SEVIRI), which are very sensitive to dust concentration, in order to reduce the frequency of the dust misclassifications encountered by the CALIPSO CAD algorithm. For the two Saharan dust events presented in this paper, both the MODIS and SEVIRI BTD CAD techniques performed well but the MODIS BTD CAD correctly reclassified more CALIPSO CAD misclassifications as dust. After applying both techniques to all the daytime CALIPSO transects over North Africa during June 2007, the MODIS and SEVIRI BTD CAD increased the total number of detected aerosol layers by approximately 10% and 4%, respectively. Even though the Version 3 (V3) CAD algorithm is significantly more accurate in deciphering between dense dust and clouds than the Version 2 algorithm, the V3 still showed some dust misclassifications among the case studies. Thus, the BTD CAD technique can help reduce the frequency of dust misclassifications encountered by the V3 CAD algorithm.

An investigation of the error budget of tropical rainfall accumulation derived from merged passive microwave and infrared satellite measurements

AbstractAn ongoing change in the theoretical framework from deterministic to probabilistic satellite rainfall estimations emerges from applications that require an error associated with rain estimates. The error budget for accumulated rainfall consists of several terms; these terms are related to sampling, algorithmic and calibration errors. From a number of studies, various errors were derived which have improved our understanding of the different terms in this error budget. In this paper, a methodological effort leading to the evaluation of a Tropical Amount of Precipitation with an Estimation of ERrors (TAPEER) is presented. It involves first merging passive microwave instantaneous rain rates from the BRAIN algorithm together with infrared imagery to build rain accumulations, and then evaluating the different terms of the error budget using two techniques. A dedicated error model is used to evaluate sampling errors and a forward error propagation approach is used for the estimation of algorithmic and calibration errors. One of the main findings in this study is the large contribution of the sampling errors and the algorithmic errors of BRAIN on medium rain rates (2–10 mm h−1) in the total error budget. This methodology leads to the formulation of a satellite rainfall product called TAPEER‐BRAIN. This product will be one of the operational rainfall products for the Megha‐Tropiques mission and will provide 1°/1‐day accumulated rainfall estimations and associated error over the whole tropical belt. Copyright © 2012 Royal Meteorological Society

Dynamics of a lava fountain revealed by geophysical, geochemical and thermal satellite measurements: The case of the 10 April 2011 Mt Etna eruption

[1] Geophysical (tilt, seismic tremor and gravity signals), geochemical (crater SO2flux) and infrared satellite measurements are presented and discussed to track the temporal evolution of the lava fountain episode occurring at Mt Etna volcano on 10 April 2011. The multi-disciplinary approach provides insight into a gas-rich magma source trapped in a shallow storage zone inside the volcano edifice. This generated the fast ascending gas-magma dispersed flow feeding the lava fountain and causing the depressurization of a deeper magma storage. Satellite thermal data allowed estimation of the amount of erupted lava, which, summed to the tephra volume, yielded a total volume of erupted products of about 1 × 106 m3. Thanks to the daylight occurrence of this eruptive episode, the SO2 emission rate was also estimated, showing a degassing cycle reaching a peak of 15,000 Mg d−1 with a mean daily value of ∼5,700 Mg d−1. The SO2 data from the previous fountain episode on 17–18 February to 10 April 2011, yielded a cumulative degassed magma volume of about ∼10.5 × 106 m3, indicating a ratio of roughly 10:1 between degassed and erupted volumes. This volumetric balance, differently from those previously estimated during different styles of volcanic activities with long-term (years) recharging periods and middle-term (weeks to months) effusive eruptions, points toward the predominant role played by the gas phase in generating and driving this lava fountain episode.

First global distributions of nitric acid in the troposphere and the stratosphere derived from infrared satellite measurements

Global distributions of nitric acid (HNO3) partial columns in the troposphere and the stratosphere are shown for the first time. HNO3 vertical profiles are retrieved from a set of high‐resolution infrared spectra, measured by the nadir‐viewing Interferometric Monitor for Greenhouse Gases (IMG) instrument onboard the ADEOS satellite. Ten successive days of IMG operation in April 1997 are analyzed, yielding quasi‐global distributions. We show that the IMG measurements contain between 0.7 and 1.8 independent pieces of information on the HNO3 vertical distribution; the extent of which depends on the observed scene. In the tropics and the midlatitudes, tropospheric and stratospheric columns can be separated, whereas, in the colder polar regions, the total column is the most relevant quantity. A detailed error budget reveals that the accuracy on the profile measurements ranges from 5–15% in the stratosphere to about 20–30% in the middle troposphere, and as much as 60% in the lowermost troposphere where the measurements are less sensitive. The global distributions of tropospheric and stratospheric partial columns are presented and discussed. In the stratosphere we measure higher columns at both poles than in the intertropical belt, as expected for the period in April analyzed here. In the troposphere, the zonal distributions show elevated values nearby NOx source regions, such as central Europe, the Eastern coast of the United States, and the North‐West of India. Typical profiles with mixing ratios up to 2 ppbv in the boundary layer and 1 ppbv in the free troposphere are identified. These novel results indicate a possible role of HNO3 in the long‐range transport of active nitrogen and open promising perspectives for future space missions dedicated to atmospheric chemistry.